渐近尺度不变多分辨率Quantization
Asymptotically Scale-invariant Multi-resolution Quantization
摘要：
多分辨率量化器是一系列量化器，其中较粗量化器的输出可以从较细量化器的输出推导出。在本文中，我们提出了一个渐进的尺度不变的多分辨率量化器，它执行均匀跨越任何选择的平均量化步骤，当输入数的范围的长度是大的。尺度不变性在最坏情况或对抗性设置中特别有用，确保量化器的性能不会受到存储或误差要求的微小变化的极大影响。我们还表明，建议的量化器实现了率和误差之间的权衡，是任意接近最佳。
英文摘要：
A multi-resolution quantizer is a sequence of quantizers where the output of a coarser quantizer can be deduced from the output of a finer quantizer. In this paper, we propose an asymptotically scale-invariant multi-resolution quantizer, which performs uniformly across any choice of average quantization step, when the length of the range of input numbers is large. Scale invariance is especially useful in worst case or adversarial settings, ensuring that the performance of the quantizer would not be affected greatly by small changes of storage or error requirements. We also show that the proposed quantizer achieves a tradeoff between rate and error that is arbitrarily close to the optimum.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
